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    {
      "cell_type": "code",
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      "source": [
        "%matplotlib inline"
      ]
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    {
      "cell_type": "markdown",
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      "source": [
        "\n# SVM-Kernels\n\n\nThree different types of SVM-Kernels are displayed below.\nThe polynomial and RBF are especially useful when the\ndata-points are not linearly separable.\n\n\n\n"
      ]
    },
    {
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      "source": [
        "print(__doc__)\n\n\n# Code source: Ga\u00ebl Varoquaux\n# License: BSD 3 clause\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn import svm\n\n\n# Our dataset and targets\nX = np.c_[(.4, -.7),\n          (-1.5, -1),\n          (-1.4, -.9),\n          (-1.3, -1.2),\n          (-1.1, -.2),\n          (-1.2, -.4),\n          (-.5, 1.2),\n          (-1.5, 2.1),\n          (1, 1),\n          # --\n          (1.3, .8),\n          (1.2, .5),\n          (.2, -2),\n          (.5, -2.4),\n          (.2, -2.3),\n          (0, -2.7),\n          (1.3, 2.1)].T\nY = [0] * 8 + [1] * 8\n\n# figure number\nfignum = 1\n\n# fit the model\nfor kernel in ('linear', 'poly', 'rbf'):\n    clf = svm.SVC(kernel=kernel, gamma=2)\n    clf.fit(X, Y)\n\n    # plot the line, the points, and the nearest vectors to the plane\n    plt.figure(fignum, figsize=(4, 3))\n    plt.clf()\n\n    plt.scatter(clf.support_vectors_[:, 0], clf.support_vectors_[:, 1], s=80,\n                facecolors='none', zorder=10, edgecolors='k')\n    plt.scatter(X[:, 0], X[:, 1], c=Y, zorder=10, cmap=plt.cm.Paired,\n                edgecolors='k')\n\n    plt.axis('tight')\n    x_min = -3\n    x_max = 3\n    y_min = -3\n    y_max = 3\n\n    XX, YY = np.mgrid[x_min:x_max:200j, y_min:y_max:200j]\n    Z = clf.decision_function(np.c_[XX.ravel(), YY.ravel()])\n\n    # Put the result into a color plot\n    Z = Z.reshape(XX.shape)\n    plt.figure(fignum, figsize=(4, 3))\n    plt.pcolormesh(XX, YY, Z > 0, cmap=plt.cm.Paired)\n    plt.contour(XX, YY, Z, colors=['k', 'k', 'k'], linestyles=['--', '-', '--'],\n                levels=[-.5, 0, .5])\n\n    plt.xlim(x_min, x_max)\n    plt.ylim(y_min, y_max)\n\n    plt.xticks(())\n    plt.yticks(())\n    fignum = fignum + 1\nplt.show()"
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